{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "[[chapter_pet_breeds]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we understand what deep learning is, what it's for, and how to create and deploy a model, it's time for us to go deeper! In an ideal world deep learning practitioners wouldn't have to know every detail of how things work under the hood… But as yet, we don't live in an ideal world. The truth is, to make your model really work, and work reliably, there's a lot of details you have to get right. And a lot of details that you have to check. This process requires being able to look inside your neural network as it trains, and as it makes predictions, find possible problems, and know how to fix them.\n",
    "\n",
    "So, from here on in the book we are going to do a deep dive into the mechanics of deep learning. What is the architecture of a computer vision model, an NLP model, a tabular model, and so on. How do you create an architecture which matches the needs of your particular domain? How do you get the best possible results from the training process? How do you make things faster? What do you have to change as your datasets change?\n",
    "\n",
    "We will start by repeating the same basic applications that we looked at in the first chapter, but we are going to do two things:\n",
    "\n",
    "- make them better;\n",
    "- apply them to a wider variety of types of data.\n",
    "\n",
    "In order to do these two things, we will have to learn all of the pieces of the deep learning puzzle. This includes: different types of layers, regularisation methods, optimisers, putting layers together into architectures, labelling techniques, and much more. We are not just going to dump all of these things out, but we will introduce them only as they are needed to solve an actual problem related to a project we are working on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From dogs and cats, to pet breeds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our very first model we learnt how to classify dogs versus cats. Just a few years ago this was considered a very challenging task. But today, it is far too easy! We will not be able to show you the nuances of training models with this problem, because we get the nearly perfect result without worrying about any of the details. But it turns out that the same dataset also allows us to work on a much more challenging problem: figuring out what breed of pet is shown in each image.\n",
    "\n",
    "In the first chapter we presented the applications as already solved problems. But this is not how things work in real life. We start with some dataset which we know nothing about. We have to understand how it is put together, how to extract the data we need from it, and what that data looks like. For the rest of this book we will be showing you how to solve these problems in practice, including all of these intermediate steps necessary to understand the data that we working with and test our modelling as we go.\n",
    "\n",
    "We have already downloaded the pets dataset. We can get a path to this dataset using the same code we saw in <<chapter_intro>>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai2.vision.all import *\n",
    "path = untar_data(URLs.PETS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we are going to understand how to extract the breed of each pet from each image were going to need to understand how this data is laid out. Such details of data layout are a vital piece of the deep learning puzzle. Data is usually provided in one of these two ways:\n",
    "\n",
    "- Individual files representing items of data, such as text documents or images, possibly organised into folders or with filenames representing information about those items, or\n",
    "- A table of data, such as in CSV format, where each row is an item, each row which may include filenames providing a connection between the data in the table and data in other formats such as text documents and images.\n",
    "\n",
    "There are exceptions to these rules, particularly in domains such as genomics, where there can be binary database formats or even network streams, but overall the vast majority of the datasets your work with use some combination of the above two formats.\n",
    "\n",
    "To see what is in our dataset we can use the ls method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "Path.BASE_PATH = path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#3) [Path('annotations'),Path('images'),Path('models')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path.ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that this dataset provides us with \"images\" and \"annotations\" directories. The website for this dataset tells us that the annotations directory contains information about where the pets are rather than what they are. In this chapter we will be doing classification, not localisation, which is to say that we care about what the pets are not where they are. Therefore we will ignore the annotations directory for now. So let's have a look inside the images directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#7394) [Path('images/great_pyrenees_173.jpg'),Path('images/wheaten_terrier_46.jpg'),Path('images/Ragdoll_262.jpg'),Path('images/german_shorthaired_3.jpg'),Path('images/american_bulldog_196.jpg'),Path('images/boxer_188.jpg'),Path('images/staffordshire_bull_terrier_173.jpg'),Path('images/basset_hound_71.jpg'),Path('images/staffordshire_bull_terrier_37.jpg'),Path('images/yorkshire_terrier_18.jpg')...]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(path/\"images\").ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most functions and methods in fastai which return a collection use a class called `L`. `L` can be thought of as an enhanced version of the ordinary Python `list` type, with added conveniences for common operations. For instance, when we display an object of this class in a notebook it appears in the format you see above. The first thing that is shown is the number of items in the collection, prefixed with a `#`. You'll also see in the above output that the list is suffixed with a \"…\". This means that only the first few items are displayed — which is a good thing, because we would not want more than 7000 filenames on our screen!\n",
    "\n",
    "By examining these filenames, we see how they appear to be structured. Each file name contains the pet breed, and then an_character, a number, and finally the file extension. We need to create a piece of code that extracts the breed from a single `Path`. Jupyter notebook makes this easy, because we can gradually build up something that works, and then use it for the entire dataset. We do have to be careful to not make too many assumptions at this point. For instance, if you look carefully you may notice that some of the pet breeds contain multiple words, so we cannot simply break at the first `_` character that we find. To allow us to test our code, let's pick out one of these filenames:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = (path/\"images\").ls()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most powerful and flexible way to extract information from strings like this is to use a *regular expression*, also known as a *regex*. A regular expression is a special string, written in the regular expression language, which specifies a general rule for for deciding if another string passes a test (i.e., \"matches\" the regular expression), and also possibly for plucking a particular part or parts out of that other string. \n",
    "\n",
    "In this case, we need a regular expressin that extracts the pet breed from the file name.\n",
    "\n",
    "We do not have the space to give you a complete regular expression tutorial here, particularly because there are so many excellent ones online. And we know that many of you will already be familiar with this wonderful tool. If you're not, that is totally fine — this is a great opportunity for you to rectify that! We find that regular expressions are one of the most useful tools in our programming toolkit, and many of our students tell us that it is one of the things they are most excited to learn about. So head over to Google and search for *regular expressions tutorial* now, and then come back here after you've had a good look around.\n",
    "\n",
    "> AG: Not only are regular expresssions dead handy, they also have interesting roots. They are \"regular\" becuase they they were originally examples of a \"regular\" language, the lowest rung within the \"Chomsky hierarchy\", a grammar classification due to the same linguist Noam Chomskey who wrote *Syntactic Structures*, the pioneering work searching for the formal grammar underlying human language. This is one of the charms of computing: it may be that the hammer you reach for every day in fact came from a space ship.\n",
    "\n",
    "When you are writing a regular expression, the best way to start is just to try it against one example at first. Let's use the `findall` method to try a regular expression against the filename of the `fname` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['great_pyrenees']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall(r'(.+)_\\d+.jpg$', fname.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This regular expression plucks out all the characters leading up to the last underscore character, as long as the subsequence characters are numerical digits and then the jpeg file extension.\n",
    "\n",
    "Now that we confirmed the regular expression works for the example, let's use it to label the whole dataset. Fastai comes with many classes to help you with your labelling. For labelling with regular expressions, we can use the `RegexLabeller` class. We can use this in the data block API that we saw in <<chapter_production>> (in fact, we nearly always use the data block API--it's so much more flexible than the simple factory methods we saw in <<chapter_intro>>):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pets = DataBlock(blocks = (ImageBlock, CategoryBlock),\n",
    "                 get_items=get_image_files, \n",
    "                 splitter=RandomSplitter(seed=42),\n",
    "                 get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'),\n",
    "                 item_tfms=Resize(460),\n",
    "                 batch_tfms=aug_transforms(size=224, min_scale=0.75))\n",
    "dls = pets.dataloaders(path/\"images\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Presizing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One important piece of this `DataBlock` call that we haven't seen before is in these two lines:\n",
    "\n",
    "```python\n",
    "item_tfms=Resize(460),\n",
    "batch_tfms=aug_transforms(size=224, min_scale=0.75)\n",
    "```\n",
    "\n",
    "These lines implement a fastai data augmentation strategy which we call *presizing*. Presizing is a particular way to do image augmentation, which is designed to minimize data destruction while maintaining good performance.\n",
    "\n",
    "We need our images to have the same dimensions, so that they can collate into tensors to be passed to the GPU. We also want to minimize the number of distinct augmentation computations we perform. So the performance requirement suggests that we should, where possible, compose our augmentation transforms into fewer transforms (to reduce the number of computations, and reduce the number of lossy operations) and transform the images into uniform sizes (to run compute efficiently on the GPU).\n",
    "\n",
    "The challenge is that, if performed after resizing down to the augmented size, various common data augmentation transforms might introduce spurious empty zones, degrade data, or both. For instance, rotating an image by 45 degrees fills corner regions of the new bounds with emptyness, which will not teach the model anything. Many rotation and zooming operations will require interpolating to create pixels. These interpolated pixels are derived from the original image data but are still of lower quality.\n",
    "\n",
    "To workaround these challenges, presizing adopts two strategies:\n",
    "\n",
    "1. First, resizing images to relatively \"large dimensions\" that is, dimensions significantly larger than the target training dimensions. \n",
    "1. Second, composing all of the common augmentation operations (including a resize to the final target size) into one, and performing the combined operation on the GPU only once at the end of processing, rather than performing them individually and interpolating multiple times.\n",
    "\n",
    "The first step, the resize, creates images large enough that they have spare margin to allow further augmentation transforms on their inner regions without creating empty zones. This transformation works by resizing to to a square, using a large crop size. On the training set, the crop area is chosen randomly, and the size of the crop is selected to cover the entire width or height of the image, whichever is smaller.\n",
    "\n",
    "In the second step, the GPU is used for all data augmentation, and all of the potentially destructive operations are done together, with a single interpolation at the end."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Presizing on the training set\" width=\"600\" caption=\"Presizing on the training set\" id=\"presizing\" src=\"images/att_00060.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This picture shows the two steps:\n",
    "\n",
    "1. *Crop full width or height*: This is in `item_tfms`, so it's applied to each individual image before it is copied to the GPU. It's used to ensure all images are the same size. On the training set, the crop area is chosen randomly. On the validation set, the center square of the image is always chosen\n",
    "2. *Random crop and augment*: This is in `batch_tfms`, so it's applied to a batch all at once on the GPU, which means it's fast. On the validation set, only the resize to the final size needed for the model is done here. On the training set, the random crop and any other augmentation is done first.\n",
    "\n",
    "To implement this process in fastai you use `Resize` as an item transform with a large size, and `RandomResizedCrop` as a batch transform with a smaller size. `RandomResizedCrop` will be added for you if you include the `min_scale` parameter in your `aug_transform` function, as you see in the `DataBlock` call above. Alternatively, you can use `pad` or `squish` instead of `crop` (the default) for the initial `Resize`.\n",
    "\n",
    "You can see in this example the difference between an image which has been zoomed, interpolated, rotated, and then interpolated again on the right (which is the approach used by all other deep learning libraries), compared to an image which has been zoomed and rotated as one operation, and then interpolated just once on the left (the fastai approach):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "dblock1 = DataBlock(blocks=(ImageBlock(), CategoryBlock()),\n",
    "                   get_y=parent_label,\n",
    "                   item_tfms=Resize(460))\n",
    "dls1 = dblock1.dataloaders([(Path.cwd()/'images'/'grizzly.jpg')]*100, bs=8)\n",
    "dls1.train.get_idxs = lambda: Inf.ones\n",
    "x,y = dls1.valid.one_batch()\n",
    "_,axs = subplots(1, 2)\n",
    "\n",
    "x1 = TensorImage(x.clone())\n",
    "x1 = x1.affine_coord(sz=224)\n",
    "x1 = x1.rotate(draw=30, p=1.)\n",
    "x1 = x1.zoom(draw=1.2, p=1.)\n",
    "x1 = x1.warp(draw_x=-0.2, draw_y=0.2, p=1.)\n",
    "\n",
    "tfms = setup_aug_tfms([Rotate(draw=30, p=1, size=224), Zoom(draw=1.2, p=1., size=224),\n",
    "                       Warp(draw_x=-0.2, draw_y=0.2, p=1., size=224)])\n",
    "x = Pipeline(tfms)(x)\n",
    "#x.affine_coord(coord_tfm=coord_tfm, sz=size, mode=mode, pad_mode=pad_mode)\n",
    "TensorImage(x[0]).show(ctx=axs[0])\n",
    "TensorImage(x1[0]).show(ctx=axs[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see here that the image on the right is less well defined, and has reflection padding artifacts in the bottom left, and the grass in the top left has disappeared entirely. We find that in practice using presizing significantly improves the accuracy of models, and often results in speedups too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking and debugging a DataBlock"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can never just assume that our code is working perfectly. We know that it works for at least one filename — but we really need to check that our dataset actually makes sense. To do this, before we do any modelling, we should always use the `show_batch` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls.show_batch(rows=1, cols=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at each image, and check that each one seems to have the correct label for that breed of pet. Often, data scientists work with data with which they are not familiar as domain experts me: for instance, I actually don't know what a lot of these pet breeds are. Since I am not an expert on pet breeds, I would use Google images at this point to search for a few of these breeds, and make sure the images looks similar to what I see in this output.\n",
    "\n",
    "If you made a mistake while building your `DataBlock` it is very likely you won't see it before this step. To debug this, we encourage you to use the `summary` method. It will attempt to create a batch from the source you give it, with a lot of details. Also, if it fails, you will see exactly at which point the error happens, and the library will try to give you some help. For instance, one common mistake is to forget to put a `Resize` transform, ending up with pictures of different sizes and not able to batch them. Here is what the summary would look like in that case (note that the exact text may have changed since the time of writing, but it will give you an idea):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting-up type transforms pipelines\n",
      "Collecting items from /home/jhoward/.fastai/data/oxford-iiit-pet/images\n",
      "Found 7390 items\n",
      "2 datasets of sizes 5912,1478\n",
      "Setting up Pipeline: PILBase.create\n",
      "Setting up Pipeline: partial -> Categorize\n",
      "\n",
      "Building one sample\n",
      "  Pipeline: PILBase.create\n",
      "    starting from\n",
      "      /home/jhoward/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_31.jpg\n",
      "    applying PILBase.create gives\n",
      "      PILImage mode=RGB size=500x414\n",
      "  Pipeline: partial -> Categorize\n",
      "    starting from\n",
      "      /home/jhoward/.fastai/data/oxford-iiit-pet/images/american_pit_bull_terrier_31.jpg\n",
      "    applying partial gives\n",
      "      american_pit_bull_terrier\n",
      "    applying Categorize gives\n",
      "      TensorCategory(13)\n",
      "\n",
      "Final sample: (PILImage mode=RGB size=500x414, TensorCategory(13))\n",
      "\n",
      "\n",
      "Setting up after_item: Pipeline: ToTensor\n",
      "Setting up before_batch: Pipeline: \n",
      "Setting up after_batch: Pipeline: IntToFloatTensor\n",
      "\n",
      "Building one batch\n",
      "Applying item_tfms to the first sample:\n",
      "  Pipeline: ToTensor\n",
      "    starting from\n",
      "      (PILImage mode=RGB size=500x414, TensorCategory(13))\n",
      "    applying ToTensor gives\n",
      "      (TensorImage of size 3x414x500, TensorCategory(13))\n",
      "\n",
      "Adding the next 3 samples\n",
      "\n",
      "No before_batch transform to apply\n",
      "\n",
      "Collating items in a batch\n",
      "Error! It's not possible to collate your items in a batch\n",
      "Could not collate the 0-th members of your tuples because got the following shapes\n",
      "torch.Size([3, 414, 500]),torch.Size([3, 375, 500]),torch.Size([3, 500, 281]),torch.Size([3, 203, 300])\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "invalid argument 0: Sizes of tensors must match except in dimension 0. Got 414 and 375 in dimension 2 at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/TH/generic/THTensor.cpp:612",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-8c0a3d421ca2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m                  \u001b[0msplitter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mRandomSplitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                  get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'))\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mpets1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m\"images\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, **kwargs)\u001b[0m\n\u001b[1;32m    172\u001b[0m         \u001b[0mwhy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_find_fail_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Make sure all parts of your samples are tensors of the same size\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mwhy\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mwhy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 174\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mf\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mafter_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfs\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'noop'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/git/fastai2/fastai2/data/block.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, source, bs, **kwargs)\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\nCollating items in a batch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mcreate_batch\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;32mreturn\u001b[0m \u001b[0mretain_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_listy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfa_convert\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprebatched\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    127\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdo_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbefore_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mone_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 46\u001b[0;31m             \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m             else default_collate(t))\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m     return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0;32m---> 46\u001b[0;31m             \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m             else default_collate(t))\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/git/fastai2/fastai2/data/load.py\u001b[0m in \u001b[0;36mfa_collate\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m     43\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m     \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m     return (default_collate(t) if isinstance(b, _collate_types)\n\u001b[0m\u001b[1;32m     46\u001b[0m             \u001b[0;32melse\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfa_collate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             else default_collate(t))\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py\u001b[0m in \u001b[0;36mdefault_collate\u001b[0;34m(batch)\u001b[0m\n\u001b[1;32m     53\u001b[0m             \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_new_shared\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnew\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__module__\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'numpy'\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'str_'\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0melem_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'string_'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: invalid argument 0: Sizes of tensors must match except in dimension 0. Got 414 and 375 in dimension 2 at /opt/conda/conda-bld/pytorch_1579022060824/work/aten/src/TH/generic/THTensor.cpp:612"
     ]
    }
   ],
   "source": [
    "#hide_output\n",
    "pets1 = DataBlock(blocks = (ImageBlock, CategoryBlock),\n",
    "                 get_items=get_image_files, \n",
    "                 splitter=RandomSplitter(seed=42),\n",
    "                 get_y=using_attr(RegexLabeller(r'(.+)_\\d+.jpg$'), 'name'))\n",
    "pets1.summary(path/\"images\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Setting-up type transforms pipelines\n",
    "Collecting items from /home/sgugger/.fastai/data/oxford-iiit-pet/images\n",
    "Found 7390 items\n",
    "2 datasets of sizes 5912,1478\n",
    "Setting up Pipeline: PILBase.create\n",
    "Setting up Pipeline: partial -> Categorize\n",
    "\n",
    "Building one sample\n",
    "  Pipeline: PILBase.create\n",
    "    starting from\n",
    "      /home/sgugger/.fastai/data/oxford-iiit-pet/images/american_bulldog_83.jpg\n",
    "    applying PILBase.create gives\n",
    "      PILImage mode=RGB size=375x500\n",
    "  Pipeline: partial -> Categorize\n",
    "    starting from\n",
    "      /home/sgugger/.fastai/data/oxford-iiit-pet/images/american_bulldog_83.jpg\n",
    "    applying partial gives\n",
    "      american_bulldog\n",
    "    applying Categorize gives\n",
    "      TensorCategory(12)\n",
    "\n",
    "Final sample: (PILImage mode=RGB size=375x500, TensorCategory(12))\n",
    "\n",
    "Setting up after_item: Pipeline: ToTensor\n",
    "Setting up before_batch: Pipeline: \n",
    "Setting up after_batch: Pipeline: IntToFloatTensor\n",
    "\n",
    "Building one batch\n",
    "Applying item_tfms to the first sample:\n",
    "  Pipeline: ToTensor\n",
    "    starting from\n",
    "      (PILImage mode=RGB size=375x500, TensorCategory(12))\n",
    "    applying ToTensor gives\n",
    "      (TensorImage of size 3x500x375, TensorCategory(12))\n",
    "\n",
    "Adding the next 3 samples\n",
    "\n",
    "No before_batch transform to apply\n",
    "\n",
    "Collating items in a batch\n",
    "Error! It's not possible to collate your items in a batch\n",
    "Could not collate the 0-th members of your tuples because got the following shapes\n",
    "torch.Size([3, 500, 375]),torch.Size([3, 375, 500]),torch.Size([3, 333, 500]),torch.Size([3, 375, 500])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see exactly how we gathered the data and split it, how we went from a filename to a *sample* (the tuple image, category), then what item transforms were applied and how it failed to collate those samples in a batch (because of the different shapes). \n",
    "\n",
    "Once you think your data looks right, we generally recommend the next step should be creating a simple model. We often see people procrastinate the training of an actual model for far too long. As a result, they don't actually get to find out what their baseline results look like. Perhaps it doesn't need lots of fancy domain specific engineering. Or perhaps the data doesn't seem to train it all. These are things that you want to know as soon as possible. So we will use the same simple model that we used in <<chapter_intro>>:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.491732</td>\n",
       "      <td>0.337355</td>\n",
       "      <td>0.108254</td>\n",
       "      <td>00:18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.503154</td>\n",
       "      <td>0.293404</td>\n",
       "      <td>0.096076</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.314759</td>\n",
       "      <td>0.225316</td>\n",
       "      <td>0.066306</td>\n",
       "      <td>00:23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
    "learn.fine_tune(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we've briefly discussed before, the table shown when we fit a model shows us the results after each epoch of training. Remember, an epoch is one complete pass through all of the images in the data. The columns shown are the average loss over the items of the training set, the loss on the validation set, and any metrics that you requested — in this case, the error rate.\n",
    "\n",
    "Remember that *loss* is whatever function we've decided to use to optimise the parameters of our model. But we haven't actually told fastai what loss function we want to use. So what is it doing? Fastai will generally try to select an appropriate loss function based on what kind of data and model you are using. In this case you have image data, and a categorical outcome, so fastai will default to using *cross entropy loss*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross entropy loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing activations and labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Cross entropy loss* is a loss function which is similar to the loss function we used in the previous chapter, but (as we'll see) has two benefits:\n",
    "\n",
    "- It works even when our dependent variable has more than two categories\n",
    "- It results in faster and more reliable training.\n",
    "\n",
    "In order to understand how cross entropy loss works for dependent variables with more than two categories, we first have to understand what the actual data and activations that are loss function is seen look like. To actually get a batch of real data from our DataLoaders, we can use the one_batch method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y = dls.one_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, this returns the dependent, and the independent variables, as a mini-batch. Let's see what is actually contained in our dependent variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorCategory([11,  0,  0,  5, 20,  4, 22, 31, 23, 10, 20,  2,  3, 27, 18, 23, 33,  5, 24,  7,  6, 12,  9, 11, 35, 14, 10, 15,  3,  3, 21,  5, 19, 14, 12, 15, 27,  1, 17, 10,  7,  6, 15, 23, 36,  1, 35,  6,\n",
       "         4, 29, 24, 32,  2, 14, 26, 25, 21,  0, 29, 31, 18,  7,  7, 17], device='cuda:5')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our batch size is 64, so we have 64 rows in this tensor. Each row is a single integer between zero and 36, representing our 37 possible pet breeds. We can view the predictions (that is, the activations of the final layer of our neural network) using `Learner.get_preds`. This function either takes a dataset index (0 for train and 1 for valid) or an iterator of batches. Thus, we can pass it a simple list with our batch to get our predictions. It returns predictions and targets by default, but since we already have the targets, we can effectively ignore them by assigning to the special variable `_`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([7.9069e-04, 6.2350e-05, 3.7607e-05, 2.9260e-06, 1.3032e-05, 2.5760e-05, 6.2341e-08, 3.6400e-07, 4.1311e-06, 1.3310e-04, 2.3090e-03, 9.9281e-01, 4.6494e-05, 6.4266e-07, 1.9780e-06, 5.7005e-07,\n",
       "        3.3448e-06, 3.5691e-03, 3.4385e-06, 1.1578e-05, 1.5916e-06, 8.5567e-08, 5.0773e-08, 2.2978e-06, 1.4150e-06, 3.5459e-07, 1.4599e-04, 5.6198e-08, 3.4108e-07, 2.0813e-06, 8.0568e-07, 4.3381e-07,\n",
       "        1.0069e-05, 9.1020e-07, 4.8714e-06, 1.2734e-06, 2.4735e-06])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds,_ = learn.get_preds(dl=[(x,y)])\n",
    "preds[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual predictions are 37 probabilities between zero and one, which add up to 1 in total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(37, tensor(1.0000))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(preds[0]),preds[0].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Softmax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An activation function called *softmax* in the final layer is used to ensure that the activations are between zero and one, and that they sum to one.\n",
    "\n",
    "Softmax is similar to the sigmoid function, which we saw earlier; sigmoid looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_function(torch.sigmoid, min=-4,max=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply this function to a single column of activations from a neural network, and get back a column of numbers between zero and one. So it's a very useful activation function for our final layer.\n",
    "\n",
    "Now think about what happens if we want to have more categories in our target (such as our 37 pet breeds). That means we'll need more activations than just a single column: we need an activation *per category*. We can create, for instance, a neural net that predicts \"3\"s and \"7\"s that returns two activations, one for each class--this will be a good first step towards creating the more general approach. Let's just use some random numbers with a standard deviation of 2 (so we multiply `randn` by 2) for this example, assuming we have six images and two possible categories (where the first columns represents \"3\"s and the second is \"7\"s):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "torch.random.manual_seed(42);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.6734,  0.2576],\n",
       "        [ 0.4689,  0.4607],\n",
       "        [-2.2457, -0.3727],\n",
       "        [ 4.4164, -1.2760],\n",
       "        [ 0.9233,  0.5347],\n",
       "        [ 1.0698,  1.6187]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acts = torch.randn((6,2))*2\n",
    "acts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can't just take the sigmoid of this directly, since we don't get rows that add to one (i.e we want the probability of being a \"3\" plus the probability of being a \"7\" to add to one):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6623, 0.5641],\n",
       "        [0.6151, 0.6132],\n",
       "        [0.0957, 0.4079],\n",
       "        [0.9881, 0.2182],\n",
       "        [0.7157, 0.6306],\n",
       "        [0.7446, 0.8346]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acts.sigmoid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In <<chapter_mnist_basics>>, the neural net created a single activation per image, which we passed through the sigmoid function. That single activation represented the confidence that the input was a \"3\". Binary problems are a special case of classification problems, because the target can be treated as a single boolean value, as we did in `mnist_loss`. Binary problems can also be thought of as part of the more general group of classifiers with any number of categories--where in this case we happen to have 2 categories. As we saw in the bear classifier, our neural net will return one activation per category.\n",
    "\n",
    "So in the binary case, what do those activations really indicate? A single pair of activations simply indicates the *relative* confidence of being a \"3\" versus being a \"7\". The overall values, whether they are both high, or both low, don't matter--all that matters it which is higher, and by how much.\n",
    "\n",
    "We would expect that since this is just another way of representing the same problem (in the binary case) that we would be able to use sigmoid directly on the two-activation version of our neural net. And indeed we can! We can just take the *difference* between the neural net activations, because that reflects how much more sure we are of being a \"3\" vs a \"7\", and then take the sigmoid of that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.6025, 0.5021, 0.1332, 0.9966, 0.5959, 0.3661])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(acts[:,0]-acts[:,1]).sigmoid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second column (the probability of being a \"7\") will then just be that subtracted from one. We need a way to do all this that also works for more than two columns. It turns out that this function, called `softmax`, is exactly that:\n",
    "\n",
    "``` python\n",
    "def softmax(x): return exp(x) / exp(x).sum(dim=1, keepdim=True)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> jargon: Exponential function (exp): Literally defined as `e**x`, where `e` is a special number approximately equal to 2.718. It is the inverse of the natural logarithm function. Note that `exp` is always positive, and it increases *very* rapidly!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check that `softmax` returns the same values as `sigmoid` for the first column, and that subtracted from one for the second column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6025, 0.3975],\n",
       "        [0.5021, 0.4979],\n",
       "        [0.1332, 0.8668],\n",
       "        [0.9966, 0.0034],\n",
       "        [0.5959, 0.4041],\n",
       "        [0.3661, 0.6339]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_acts = torch.softmax(acts, dim=1)\n",
    "sm_acts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Softmax is the multi-category equivalent of sigmoid--we have to use it any time we have more than two categories, and the probabilities of the categories must add to one. (We often use it even when there's just two categories, just to make things a bit more consistent.) We could create other functions that have the properties that all activations are between zero and one, and sum to one; however, no other function has the same relationship to the sigmoid function, which we've seen is smooth and symmetric. Also, we'll see shortly that the softmax function works well hand-in-hand with the loss function we will look at in the next section.\n",
    "\n",
    "If we have three output activations, such as in our bear classifier, calculating softmax for a single bear image would then look like something like this:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Bear softmax example\" width=\"280\" id=\"bear_softmax\" src=\"images/att_00062.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does this function do in practice? Taking the exponential ensures all our numbers are positive, and then dividing by the sum ensures we are going to have a bunch of numbers that add up to one. The exponential also has a nice property: if one of the numbers in our activations `x` is slightly bigger than the others, the exponential will amplify this (since it grows, well... exponentially) which means that in the softmax, that number will be closer to 1. \n",
    "\n",
    "Intuitively, the Softmax function *really* wants to pick one class among the others, so it's ideal for training a classifier when we know each picture has a definite label. (Note that it may be less ideal during inference, as you might want your model to sometimes tell you it doesn't recognize any of the classes is has seen during training, and not pick a class because it has a slightly bigger activation score. In this case, it might be better to train a model using multiple binary output columns, each using a sigmoid activation.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we calculated the loss for our MNIST example in the last chapter we used.\n",
    "\n",
    "```python\n",
    "def mnist_loss(inputs, targets):\n",
    "    inputs = inputs.sigmoid()\n",
    "    return torch.where(targets==1, 1-inputs, inputs).mean()\n",
    "```\n",
    "\n",
    "Just like we moved from sigmoid to softmax, we need to extend the loss function to work with more than just binary classification, to classifying any number of categories (in this case, we have 37 categories). Our activations, after softmax, are between zero and one, and sum to one for each row in the batch of predictions. Our targets are integers between 0 and 36.\n",
    "\n",
    "In the binary case, we used `torch.where` to select between `inputs` and `1-inputs`. When we treat a binary classification as a general classification problem with two categories, it actually becomes even easier, because (as we saw in the softmax section) we now have two columns, containing the equivalent of `inputs` and `1-inputs`. So all we need to do is select from the appropriate column. Let's try to implement this in PyTorch. For our synthetic \"3\"s and \"7\" example, let's say these are our labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "targ = tensor([0,1,0,1,1,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and these are the softmax activations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.6025, 0.3975],\n",
       "        [0.5021, 0.4979],\n",
       "        [0.1332, 0.8668],\n",
       "        [0.9966, 0.0034],\n",
       "        [0.5959, 0.4041],\n",
       "        [0.3661, 0.6339]])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm_acts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then for each item of `targ` we can use that to select that column of `sm_acts` using tensor indexing, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.6025, 0.4979, 0.1332, 0.0034, 0.4041, 0.3661])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = range(6)\n",
    "sm_acts[idx, targ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see exactly what's happening here, let's put all the columns together in a table. Here, the first two columns are out activations, then we have the targets, the row index, and finally the result shown immediately above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table ><thead>    <tr>        <th class=\"col_heading level0 col0\" >3</th>        <th class=\"col_heading level0 col1\" >7</th>        <th class=\"col_heading level0 col2\" >targ</th>        <th class=\"col_heading level0 col3\" >idx</th>        <th class=\"col_heading level0 col4\" >loss</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col0\" class=\"data row0 col0\" >0.602469</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col1\" class=\"data row0 col1\" >0.397531</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col2\" class=\"data row0 col2\" >0</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col3\" class=\"data row0 col3\" >0</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow0_col4\" class=\"data row0 col4\" >0.602469</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col0\" class=\"data row1 col0\" >0.502065</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col1\" class=\"data row1 col1\" >0.497935</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col2\" class=\"data row1 col2\" >1</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col3\" class=\"data row1 col3\" >1</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow1_col4\" class=\"data row1 col4\" >0.497935</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col0\" class=\"data row2 col0\" >0.133188</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col1\" class=\"data row2 col1\" >0.866811</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col2\" class=\"data row2 col2\" >0</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col3\" class=\"data row2 col3\" >2</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow2_col4\" class=\"data row2 col4\" >0.133188</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col0\" class=\"data row3 col0\" >0.99664</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col1\" class=\"data row3 col1\" >0.00336017</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col2\" class=\"data row3 col2\" >1</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col3\" class=\"data row3 col3\" >3</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow3_col4\" class=\"data row3 col4\" >0.00336017</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col0\" class=\"data row4 col0\" >0.595949</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col1\" class=\"data row4 col1\" >0.404051</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col2\" class=\"data row4 col2\" >1</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col3\" class=\"data row4 col3\" >4</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow4_col4\" class=\"data row4 col4\" >0.404051</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col0\" class=\"data row5 col0\" >0.366118</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col1\" class=\"data row5 col1\" >0.633882</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col2\" class=\"data row5 col2\" >0</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col3\" class=\"data row5 col3\" >5</td>\n",
       "                        <td id=\"T_74b4f58a_5696_11ea_af1d_53ec6ce9fc9drow5_col4\" class=\"data row5 col4\" >0.366118</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hide_input\n",
    "from IPython.display import HTML\n",
    "df = pd.DataFrame(sm_acts, columns=[\"3\",\"7\"])\n",
    "df['targ'] = targ\n",
    "df['idx'] = idx\n",
    "df['loss'] = sm_acts[range(6), targ]\n",
    "t = df.style.hide_index()\n",
    "#To have html code compatible with our script\n",
    "html = t._repr_html_().split('</style>')[1]\n",
    "html = re.sub(r'<table id=\"([^\"]+)\"\\s*>', r'<table >', html)\n",
    "display(HTML(html))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this table, you can see that the final column can be calculated by taking the `targ` and `idx` columns as indices into the 2-column matrix containing the `3` and `7` columns. That's what `sm_acts[idx, targ]` is actually doing.\n",
    "\n",
    "The really interesting thing here is that this actually works just as well with more than two columns. To see this, consider what would happen if we added a activation column above for every digit (zero through nine), and then `targ` contained a number from zero to nine. As long as the activation columns sum to one (as they will, if we use softmax), then we'll have a loss function that shows how well we're predicting each digit.\n",
    "\n",
    "We're only picking the loss from the column containing the correct label. We don't to consider the other columns, because by the definition of softmax, they add up to one minus the activation corresponding to the correct label. Therefore, making the activation for the correct label as high as possible, must mean we're also decreasing the activations of the remaining columns.\n",
    "\n",
    "PyTorch provides a function that does exactly the same thing as `sm_acts[range(n), targ]` (except it takes the negative, since we want a smaller loss to be better), called `nll_loss` (*NLL* stands for *negative log likelihood*):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.6025, -0.4979, -0.1332, -0.0034, -0.4041, -0.3661])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-sm_acts[idx, targ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.6025, -0.4979, -0.1332, -0.0034, -0.4041, -0.3661])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.nll_loss(sm_acts, targ, reduction='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Taking the `log`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This does work quite well as a loss function, but we can make it a bit better. The problem is that we are using probabilities, and probabilities cannot be smaller than zero, or greater than one. But that means that our model will not care about whether it predicts 0.99 versus 0.999, because those numbers are so close together. But in another sense, 0.999 is 10 times more confident than 0.99. So we wish to transform our numbers between zero and one to instead be between negative infinity and infinity. There is a function available in maths which does exactly this: the logarithm (available as `torch.log`). It is not defined for numbers less than zero, and looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_function(torch.log, min=0,max=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does \"logarithm\" ring a bell? The logarithm function has this identity:\n",
    "\n",
    "```\n",
    "y = a**b\n",
    "a = log(y,b)\n",
    "```\n",
    "\n",
    "In this case, we're assuming that `log(y,b)` returns *log y base b*. However, PyTorch actually doesn't define `log` this way: `log` in Python uses the special number `e` (2.718...) as the base.\n",
    "\n",
    "Perhaps a logarithm is something that you have not thought about for the last 20 years or so. But it's a mathematical idea which is going to be really critical for many things in deep learning, so now would be a great time to refresh your memory. The key thing to know about logarithms is this relationship:\n",
    "\n",
    "    log(a*b) = log(a)+log(b)\n",
    "\n",
    "When we see it in that format looks a bit boring; but have a think about what this really means. It means that logarithms increase linearly when the underlying signal increases exponentially or multiplicatively. This is used for instance in the Richter scale of earthquake severity, and the dB scale of noise levels. It's also often used on financial charts, where we want to show compound growth rates more clearly. Computer scientists love using logarithms, because it means that modification, which can create really really large and really really small numbers, can be replaced by addition, which is much less likely to result in scales which are difficult for our computer to handle."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> s: It's not just computer scientists that love logs! Until computers came along, engineers and scientists used a special ruler called a \"slide rule\" that did multiplication by adding logarithms. Logarithms are widely used in physics, for multiplying very big or very small numbers, and many other fields."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking the mean of the positive or negative log of our probabilities (depending on whether it's the correct or incorrect class) gives us the *negative log likelihood* loss. In PyTorch, `nll_loss` assumes that you already took the log of the softmax, so it doesn't actually do the logarithm for you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> warning: The \"NLL\" in \"nll_loss\" stands for \"negative log likelihood\", but it doesn't actually take the log at all! It assumes you have _already_ taken the log. PyTorch has a function called \"log_softmax\" which combines \"log\" and \"softmax\" in a fast and accurate way."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we first take the softmax, and then the log likelihood of that, that combination is called *cross entropy loss*. In PyTorch, this is available as `nn.CrossEntropyLoss` (which, in practice, actually does `log_softmax` and then `nll_loss`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_func = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see, this is a class. Instantiating it gives you an object which behaves like a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.8045)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss_func(acts, targ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All PyTorch loss functions are provided in two forms: the class form seen above, and also a plain functional form, available in the `F` namespace:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.8045)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "F.cross_entropy(acts, targ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Either one works fine and can be used in any situation. We've noticed that most people tend to use the class version, and that's more often used in PyTorch official docs and examples, so we'll tend to use that too.\n",
    "\n",
    "By default PyTorch loss functions take the mean of the loss of all items. You can use `reduction='none'` to disable that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.5067, 0.6973, 2.0160, 5.6958, 0.9062, 1.0048])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn.CrossEntropyLoss(reduction='none')(acts, targ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> s: An interesting feature about cross entropy loss appears when we consider its gradient. The gradient of `cross_entropy(a,b)` is just `softmax(a)-b`. Since `softmax(a)` is just the final activation of the model, that means that the gradient is proportional to the difference between the prediction and the target. This is the same as mean squared error in regression (assuming there's no final activation function such as that added by `y_range`), since the gradient of `(a-b)**2` is `2*(a-b)`. Since the gradient is linear, that means that we won't see sudden jumps or exponential increases in gradients, which should lead to smoother training of models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very hard to interpret loss functions directly, because they are designed to be things which computers can differentiate and optimise, not things that people can understand. That's why we have metrics. These are not used in the optimisation process, but just used to help us poor humans understand what's going on. In this case, our accuracy is looking pretty good already! So where are we making mistakes?\n",
    "\n",
    "We saw in <<chapter_intro>> that we can use a confusion matrix to see where our model is doing well, and where it's doing badly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#width 600\n",
    "interp = ClassificationInterpretation.from_learner(learn)\n",
    "interp.plot_confusion_matrix(figsize=(12,12), dpi=60)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh dear, in this case, a confusion matrix is very hard to read. We have 37 different breeds of pet, which means we have 37×37 entries in this giant matrix! Instead, we can use the `most_confused` method, which just shows us the cells of the confusion matrix with the most incorrect predictions (here with at least 5 or more):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('american_pit_bull_terrier', 'staffordshire_bull_terrier', 10),\n",
       " ('Ragdoll', 'Birman', 6)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interp.most_confused(min_val=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are not pet breed experts, it is hard for us to know whether these category errors reflect actual difficulties in recognising breeds. So again, we turn to Google. A little bit of googling tells us that the most common category errors shown here are actually breed differences which even expert breeders sometimes disagree about. So this gives us some comfort that we are on the right track."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Improving our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning rate finder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the most important things we can do when training a model is to make sure that we have the right learning rate. If our learning rate is too low, it's can take many many epochs. Not only does this waste time, but it also means that we may have problems with overfitting, because every time we do a complete pass through the data, we give our model a chance to memorise it.\n",
    "\n",
    "So let's just make our learning rate really high, right? Sure, let's try that and see what happens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8.946717</td>\n",
       "      <td>47.954632</td>\n",
       "      <td>0.893775</td>\n",
       "      <td>00:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7.231843</td>\n",
       "      <td>4.119265</td>\n",
       "      <td>0.954668</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
    "learn.fine_tune(1, base_lr=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That did not look good. Here's what happened. The optimiser stepped in the correct direction, but it stepped so far that it totally overshot the minimum loss. Repeating that multiple times makes it get further and further away, not closer and closer!\n",
    "\n",
    "What do we to find the perfect learning rate, not too high, and not too low? In 2015 the researcher Leslie Smith came up with a brilliant idea, called the *learning rate finder*. His idea was to start with a very very small learning rate, something so small that we would never expect it to be too big to handle. We use that for one mini batch, find what the losses afterwards, and then increase the learning rate by some percentage (e.g. doubling it each time). Then we do another mini batch, track the loss, and double the learning rate again. We keep doing this until the loss gets worse, instead of better. This is the point where we know we have gone too far. We then select a learning rate a bit lower than this point. Our advice is to pick either:\n",
    "\n",
    "- one order of magnitude less than where the minimum loss was achieved (i.e. the minimum divided by 10)\n",
    "- the last point where the loss was clearly decreasing. \n",
    "\n",
    "The Learning Rate Finder computes those points on the curve to help you. Both these rules usually give around the same value. In the first chapter, we didn't specified a learning rate, using the default value from the fastai library (which is 1e-3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
    "lr_min,lr_steep = learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum/10: 8.32e-03, steepest point: 6.31e-03\n"
     ]
    }
   ],
   "source": [
    "print(f\"Minimum/10: {lr_min:.2e}, steepest point: {lr_steep:.2e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see on this plot that in the range 1e-6 to 1e-3, nothing really happens and the model doesn't train. Then the loss starts to decrease until it reaches a minimum then increases again. We don't want a learning rate greater than 1e-1 as it will give a training that diverges (you can try for yourself) but 1e-1 is already too high: at this stage we left the period where the loss was decreasing steadily.\n",
    "\n",
    "In this learning rate plot it appears that a learning rate around 3e-3 would be appropriate, so let's choose that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: The learning rate finder plot has a logarithmic scale, which is why the middle point between 1e-3 and 1e-2 is between 3e-3 and 4e-3. This is because we care mostly about the order of magnitude of the learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.071820</td>\n",
       "      <td>0.427476</td>\n",
       "      <td>0.133965</td>\n",
       "      <td>00:19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.738273</td>\n",
       "      <td>0.541828</td>\n",
       "      <td>0.150880</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.401544</td>\n",
       "      <td>0.266623</td>\n",
       "      <td>0.081867</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
    "learn.fine_tune(2, base_lr=3e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something really interesting about this is that it was only discovered in 2015. Neural networks have been under development since the 1950s. Throughout that time finding a good learning rate has been, perhaps, the most important and challenging issue for practitioners. The idea does not require any advanced maths, giant computing resources, huge datasets, or anything else that would make it inaccessible to any curious researcher. Furthermore, the researcher who did develop it, Leslie Smith, was not part of some exclusive Silicon Valley lab, but was working as a naval researcher. All of this is to say: breakthrough work in deep learning absolutely does not require access to vast resources, elite teams, or advanced mathematical ideas. There is lots of work still to be done which requires just a bit of common sense, creativity, and tenacity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unfreezing and transfer learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We discussed briefly in <<chapter_intro>> how transfer learning works. We saw that the basic idea is that a pretrained model, trained potentially on millions of data points (such as ImageNet), is fine tuned for some other task. But what does this really mean?\n",
    "\n",
    "We now know that a convolutional neural network consists of many layers with a non-linear activation function between each and one or more final linear layers, with an activation functions such as softmax at the very end. The final linear layer uses a matrix with enough columns such that the output size is the same as the number of classes in our model (assuming that we are doing classification).\n",
    "\n",
    "This final linear layer is unlikely to be of any use for us, when we are fine tuning in a transfer learning setting, because it is specifically designed to classify the categories in the original pretraining dataset. So when we do transfer learning we remove it, and throw it away, and replace it with a new linear layer with the correct number of outputs for our desired task (in this case, there would be 37 activations).\n",
    "\n",
    "This newly added linear layer will have entirely random weights. Therefore, our model prior to fine tuning has entirely random outputs. But that does not mean that it is an entirely random model! All of the layers prior to the last one have been carefully trained to be good at image classification tasks in general. As we saw in the images from the Zeiler and Fergus paper, the first layers encode very general concepts such as finding gradients and edges, and later layers encode concepts that are still very useful for us, such as finding eyeballs and fur.\n",
    "\n",
    "We want to train a model in such a way that we allow it to remember all of these generally useful ideas from the pretrained model, use them to solve our particular task (classify pet breeds), and only adjust them as required for the specifics of our particular task.\n",
    "\n",
    "Our challenge than when fine tuning is to replace the random weights in our added linear layers with weights that correctly achieve our desired task (classifying pet breeds) without breaking the carefully pretrained weights and the other layers. There is actually a very simple trick to allow this to happen: tell the optimiser to only update the weights in those randomly added final layers. Don't change the weights in the rest of the neural network at all. This is called *freezing* those pretrained layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we create a model from a pretrained network fastai automatically freezes all of the pretrained layers for us. When we call the `fine_tune` method fastai does two things:\n",
    "\n",
    "- train the randomly added layers for one epoch, with all other layers frozen ;\n",
    "- unfreeze all of the layers, and train them all for the number of epochs requested.\n",
    "\n",
    "Although this is a reasonable default approach, it is likely that for your particular dataset you may get better results by doing things slightly differently. The `fine_tune` method has a number of parameters you can use to change its behaviour, but it might be easiest for you to just call the underlying methods directly. Remember that you can see the source code for the method by using the following syntax:\n",
    "\n",
    "    learn.fine_tune??\n",
    "\n",
    "So let's try doing this manually ourselves. First of all we will train the randomly added layers for three epochs, using `fit_one_cycle`. As mentioned in <<chapter_intro>>, `fit_one_cycle` is the suggested way to train models without using `fine_tune`. We'll see why later in the book; in short, what `fit_one_cycle` does is to start training at a low learning rate, gradually increase it for the first section of training, and then gradually decrease it again for the last section of training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.188042</td>\n",
       "      <td>0.355024</td>\n",
       "      <td>0.102842</td>\n",
       "      <td>00:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.534234</td>\n",
       "      <td>0.302453</td>\n",
       "      <td>0.094723</td>\n",
       "      <td>00:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.325031</td>\n",
       "      <td>0.222268</td>\n",
       "      <td>0.074425</td>\n",
       "      <td>00:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
    "learn.fit_one_cycle(3, 3e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we will unfreeze the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and run `lr_find` again, because having more layers to train, and weights that have already been trained for 3 epochs, means our previously found learning rate isn't appropriate and more:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1.0964782268274575e-05, 1.5848931980144698e-06)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the graph is a little different from when we had random weights: we don't have that sharp descent that indicates the model is training. That's because our model has been trained already. Here we have a somewhat flat area before a sharp increase, and we should take a point well before that sharp increase, for instance 1e-5. The point with the maximum gradient isn't what we look for here and should be ignored.\n",
    "\n",
    "Let's train at a suitable learning rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
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       "      <td>00:24</td>\n",
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       "      <td>00:24</td>\n",
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       "    <tr>\n",
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       "      <td>0.061570</td>\n",
       "      <td>00:24</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.194269</td>\n",
       "      <td>0.200149</td>\n",
       "      <td>0.059540</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.173164</td>\n",
       "      <td>0.202301</td>\n",
       "      <td>0.059540</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(6, lr_max=1e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This has improved our model a bit, but there's more we can do..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discriminative learning rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even after we unfreeze, we still care a lot about the quality of those pretrained weights. We would not expect that the best learning rate for those pretrained parameters would be as high as the randomly added parameters — even after we have tuned those randomly added parameters for a few epochs. Remember, the pretrained weights have been trained for hundreds of epochs, on millions of images.\n",
    "\n",
    "In addition, do you remember the images we saw in <<chapter_intro>>, showing what each layer learns? The first layer learns very simple foundations, like edge and gradient detectors; these are likely to be just as useful for nearly any task. The later layers learn much more complex concepts, like \"eye\" and \"sunset\", which might not be useful in your task at all (maybe you're classifying car models, for instance). So it makes sense to let the later layers fine-tune more quickly than earlier layers.\n",
    "\n",
    "Therefore, fastai by default does something called *discriminative learning rates*. This was originally developed in the ULMFiT approach to NLP transfer learning that we introduced in <<chapter_intro>>. Like many good ideas in deep learning, it is extremely simple: use a lower learning rate for the early layers of the neural network, and a higher learning rate for the later layers (and especially the randomly added layers). The idea is based on insights developed by Jason Yosinski, who showed in 2014 that when transfer learning different layers of a neural network should train at different speeds:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img alt=\"Impact of different layers and training methods on transfer learning (Yosinski)\" width=\"680\" caption=\"Impact of different layers and training methods on transfer learning (Yosinski)\" id=\"yosinski\" src=\"images/att_00039.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fastai lets you pass a Python *slice* object anywhere that a learning rate is expected. The first value past will be the learning rate in the earliest layer of the neural network, and the second value will be the learning rate in the final layer. The layers in between will have learning rates that are multiplicatively equidistant throughout that range. Let's use this approach to replicate the previous training, but this time we'll only set the *lowest* layer of our net to a learning rate of `1e-6`; the other layers will scale up to `1e-4`. Let's train for a while and see what happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
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       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
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       "      <td>00:20</td>\n",
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       "      <td>00:20</td>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>0.257977</td>\n",
       "      <td>0.205400</td>\n",
       "      <td>0.067659</td>\n",
       "      <td>00:25</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.246763</td>\n",
       "      <td>0.205107</td>\n",
       "      <td>0.066306</td>\n",
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       "    <tr>\n",
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       "      <td>0.240595</td>\n",
       "      <td>0.193848</td>\n",
       "      <td>0.062246</td>\n",
       "      <td>00:25</td>\n",
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       "      <td>0.209988</td>\n",
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       "      <td>0.062923</td>\n",
       "      <td>00:25</td>\n",
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       "      <td>0.169985</td>\n",
       "      <td>0.187885</td>\n",
       "      <td>0.056157</td>\n",
       "      <td>00:25</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
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       "      <td>0.186145</td>\n",
       "      <td>0.058863</td>\n",
       "      <td>00:25</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.141480</td>\n",
       "      <td>0.185316</td>\n",
       "      <td>0.053451</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.128564</td>\n",
       "      <td>0.180999</td>\n",
       "      <td>0.051421</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.126941</td>\n",
       "      <td>0.186288</td>\n",
       "      <td>0.054127</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.130064</td>\n",
       "      <td>0.181764</td>\n",
       "      <td>0.054127</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.124281</td>\n",
       "      <td>0.181855</td>\n",
       "      <td>0.054127</td>\n",
       "      <td>00:25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
    "learn.fit_one_cycle(3, 3e-3)\n",
    "learn.unfreeze()\n",
    "learn.fit_one_cycle(12, lr_max=slice(1e-6,1e-4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the fine tuning is working great!\n",
    "\n",
    "Fastai can show us a graph of the training and validation loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s+cU12wHKKqs5WlLBHz/a4ioBPHjpMAA+/c25vHvLxBrHR4YE8vJ1Y3j6ypFNyvOArlH8L+NIjeqfX7+9vsYxxhjeXL2P+/6bXmNsQ0ELrGWglOp4fJkLyFvltdchsiJyLZAKTPZI7mWMyRGRfsAyEdkMeJvwxus1jTEvAy8DpKamnvSqzkF2HXyVw0GlPR9QY+KiQuqsIXD/R1v4T1pWjbSxHiUIz4XlrWuEcsGwxCbnd0zvWL7edaTBKSmcbQQA2cdOuKq0dF0BpZQvfCkBZAM9Pd4nA3XqIkRkGnAvcIkxxtWB3RiTY//dA6wARgFHgM4i4gxAXq/ZnJxTQldWG6ocxqcpohOiQjlQUFpjSojlO+q2CcR7DOCKjazZlu1LA643cfZ1Dh6vWbfv2aD95S53ieiZZRkkxYSREBXK+v3HfJrGQinl33wJAGuBgXavnRBgNrDA8wARGQW8hPXwz/VIjxWRUHs7Hjgb2Gqsp9NyYJZ96PXAR6d6Mw0JCrButcrhoMphXO8bktQ5nLJKB8+v2O1KCwyoGzi6eDz0o0KDuPP7g7l92kDAew8jX8TZQcVzqglwLxqzaPNBflOrSig5NoLconLW7D3KG6v2ndTnKqX8R6NPQbue/lZgMbANeMcYs0VEHhQRZ6+eJ4Eo4N1a3T3PANJEZCPWA/8xY8xWe9/dwO9EJAOrTeBfzXZXXgR5lgCqHQR5eZDXdsFQa0DYk4t3UO0wOByGvKK6o3NrB4VfTR3AbecP5I4LBvGHH9TuMOWbnrERAPz7f5mAe3qKr3dav/q3Hqxbi5Yc625oXrU7v85+pZTy5NN6AMaYhcDCWml/9NieVs95K4ER9ezbg9XD6LRwTgjnagT2oQqoX0KUa/vAsVIiQgOpamhleQ8iwq3nDTy5zAIDu0XVeD91cFeeXLyDOR9sZuaoHjVmFZ08KIEvd+YxtHsMY/rEcu+H6T5NZKeU8m9+MRIYcP3ir3I4KKmoqrFIS0Pm3TQegD1Hisk9jXPzhAUHMrZPrOt9987uxuWvdx3h8y2HAPjm7qmuCenOG9KVa8b35srUnmw9WNjkdgBjDEu2HtaRxEr5Cf8JAHYJ4GhJBdnHSokO8y0A9I63uokePF7GYXtunmlndOXnk/vxvaHduHXqgJbJMPDaDeO4cEQiX981lc4RIXzwy7MA+OvnO9hu905Kjo3giVkpPHTpMFeJZWj3GI6WVDR5Mrn31mVz0xtpvNvA4jZKqY7DL5aEBFyzcT69ZBcAe46U+HSec4rmQ8fLCLSrXe6/eBg9u0S0QC5rigwN4vlrxrjej+5llQi21+qa2ic+kj7x7vEMzgFpa/bmc+nIhoZs1LQ+qwCAE7oOgVJ+wW9KALWnfPZ10feQoADiIkPIKy7n4HGrBJBQa9rm06mvx4P+/ou9NzCfkRRDp/BgXlixm9yiMgp9HBnsDJIn23VVKdW++E0ACAiQGnPyXzO+dwNH11Re5WDemv08tWQnXaNDT7prZ3O4e/pg13ZKcmevx0SGBnHTOX3ZfqiIcY8sZcqTK3y6trPN4HChTkOtlD/wmwAAEBps3W6XyBAuG9PgdEU1ePaoSfJxTp+W0jnCPeYgpoF2DM8qoaM+TGsN7qqftMxjJ5k7pVR74lcBwPnLvVN48Elfo6GH7unQz+PB3rXWtBOezkiKrvE+y4fZSE/YVT+HtASglF/wqwDgrAIa0aNTk877nj0gDCAipPWqf6DmQ7+hQDaga80AsCXneKPXdnYnPXaiQruCKuUH/CoAHCiw5tXxbEj1xcvXjXH98p86uGuz56up5v1sPE/OSmnSObvzGu71ZIxh2fZcexuO6YRySnV4ftMN1NPUIU17iIsIG++/gO/2H3N1xWxNZ/WP9+m4+y46g71HSliwMcfrFBaejteaQjq/pLxVezsppVqeXwWAK1N7UulwMLKn994zDRERxvSuu3BMW3bTuf0AWLk7nyPFDQeAhz7ZBsBlo5N5/7ts8osr+MviHSzYmMNXd01t8bwqpU4/vwoAjzex2qSjiIsMqbOwTW3vf2eN/u0TZw1wu+aVNa59eUVaGlCqI/KrNgB/FR8V2mgJwGlA16g6aZn5vo2aVkq1LxoA/EBsZAi7covrHeFb6dHjJ7FT3a6lh45rt1ClOiINAH7AuWD8Cx4L23jynBZjQNcofj6pX439GgCU6pg0APiB+y6y5gyqXZWTfuA4FVUO17rDT8xKITosmNum1VzHoKGBYQ6H4X8ZR2qUIpRS7YMGAD8wODGacX27cLDA/SDfnH2cHzz7DYPuW+QaJexc0D48OJBxfbowZ8YQ+sZHNhgA/r0yk2teWcNie30CpVT74Ve9gPxZcudwVnusL1zkMUPom6ut9YOdDcAiwju3TASsLqSZR0o4fqKSmPCgGiuRAWTkWlNTazWRUu2PlgD8RHJsOIcKy1xVNSUec/4vSrd+vXsubu80JDGaLTmFnPng58xfm1Vnv8Ou+Vm/v6AFcq2Uakk+BQARmS4iO0QkQ0TmeNn/OxHZKiKbRGSpiPS200eKyCoR2WLvu9LjnNdEZK+9iPwGERnZfLelausRG47DuH+pe1sPofaaCWAFACdv1TzHTljjC77eldfkJSiVUq2r0QAgIoHAc8AMYChwlYjUXolkPZBqjEkB3gOesNNPAD82xgwDpgNPi4jnMNw7jTEj7deGU7wX1YAena0BXtnHrPmQvC0aX7t6B2B8vzjXBHgrduTxz6/2uPZVVDlYuduqViosqyK3kekmlFJtiy8lgHFAhjFmjzGmApgPXOp5gDFmuTHGOd/waiDZTt9pjNllb+cAuUBCc2Ve+S451lrH4Kp/rmbxlkMU2L/c/3H1qAbP69E5nK0PTneVBB5ZuM2179p/raG4vIprxvcC4KudeS2RdaVUC/ElAPQAPCt/s+20+twILKqdKCLjgBDAszP6I3bV0FMi4nWuARG5WUTSRCQtL08fMCerZ5cI4qOsOv6fv7mOv3y+k/DgQC4akeTT+TNHub/yb/ceBSDHnl31lsn9CRDf1hxQSrUdvgSAuvUC4LWyV0SuBVKBJ2ulJwFvAjcYY5wdxu8BhgBjgS7A3d6uaYx52RiTaoxJTUjQwsPJCgwQ/vCDmjV3pZXViAjzbhrPP3+c2uD5P5/Uj6vtX/pXvLQKgKFJMQxJjLaDS6guJKNUO+NLN9BsoKfH+2Qgp/ZBIjINuBeYbIwp90iPAT4F7jPGrHamG2MO2pvlIvJv4I6mZ181xZjesQQGCNUOK36n9ramtj5rQOPTS4sIgR5tBFlHT/D51sOu94mdwjhUqG0ASrUnvpQA1gIDRaSviIQAs4EFngeIyCjgJeASY0yuR3oI8CHwhjHm3VrnJNl/BZgJpJ/KjajGJcdGsPvPF9LDXtf4rZvGN+n8n53bj55drHP/sSyjxr5uMWEc1rEASrUrjQYAY0wVcCuwGNgGvGOM2SIiD4rIJfZhTwJRwLt2l05ngLgCmAT8xEt3z7kishnYDMQDDzffbamGfPirs1hw69muNZJ91Ssugq/unErf+EgWbKxZCEyMCSPr2Il6J5xTSrU90p76bqemppq0tLTWzobfe3ThNl6yu4P+7Yoz+dHoZJZvz+WG19YC8N0fvud1UJlSqnWIyDpjTJ2GPh0JrJqsX4J7TeURPToBcO5AdzvCNxlHTnuelFJNpwFANZlzUBlAdFgwAEGB7n9Kh46XnvY8KaWaTgOAarIe9qAywDW2AODFa0cDcOi49gZSqj3QAKCaLMlj1TDPX/7ThyeRktyJbzK8D9j7ZFMO2w4Wtnj+lFK+0emgVZOFBQfy+wuH0L1zeJ19UwYl8OzyDCqqHIR4TC7ncBhunbcegMzHLjpteVVK1U8DgDopN0/q7zW9Z5cIjIH0nOOM7hXrSnfOGqqUaju0Ckg1q5Rka7LXK15cVSPdc5oI50hkpVTr0gCgmtWgbtaqYlW1HvK5HtNEOCeRU0q1Lg0AqlmJCBelWDOMeq4dcNijBKDjBJRqGzQAqGbnnGTukYXb2JhlLRXpWQV0RBeOUapN0ACgmt1lY5Jd2698s5eKKgeHC8uJjwohOjSIg4Vl7MsvacUcKqVAewGpFhAd6v5n9fHGHJZtO+xahL5ffCTz1uxn3pr9PHDJMK4/q08r5VIppSUA1exEhL9cfiZnJlvzBDkf/rERwUzsH+c67pmlu1olf0opiwYA1SJmjUnm2atG10h79SdjaywtWbunkFLq9NIAoFpMr7gIZgxPdL0f1SuWsX260N2eSqKwrJKqakd9pyulWpgGgOZQcQLKi6y/VeXgqIZ2tM5CS5ruEQCcVtw5lXsvPANj4HhpZSvkSikF2gjcPBbdCevfqpsuARAQBBIIAfbLte1Mr31MkH2e89ggezsAgsJg+GUw4nIIbB9f3aUje3Db/A18f1g3V1pIUABJna1SwOsrM/ndBYNbK3tK+TVdEaw5ZCyFw1vAVFu//h3VHttV9rbDY9uZ7qh1TCPnluTC0T3QpT9MvguGz2oXgaCiykFQgBAQ4F5U/uDxUiY+ugyAzX+6wLWugFKq+dW3IpgGgPbEGNj+Kax4DA5vhrgBMOkuGDHLKiW0M88tz+DJxTvo0Tmcq8f34ldTB/DCit3ERgQze1yv1s6eUh2GLgnZEYjAGT+An38FV7xpVQl9eDM8Nx42vWuVGtqRi0ZYU0YcKCjlycU72HawkMc/286cDza3cs6U8g8+BQARmS4iO0QkQ0TmeNn/OxHZKiKbRGSpiPT22He9iOyyX9d7pI8Rkc32NZ8REal9XVWPgAAYegn8/Gu44g0IDIYPboLnJ8Dm99pNIEj0WFgGYMbfv3ZtV55i76AlWw+zek/+KV1DqY6u0QAgIoHAc8AMYChwlYgMrXXYeiDVGJMCvAc8YZ/bBbgfGA+MA+4XEeck8S8ANwMD7df0U74bfxMQAEMvhVv+B5e/bjUYv38jPD+xXQSCsOD6q6325JVQXF7FbfPXk3mkhD/8N51XvnZPLudwGFbvyae+Ksyb3khj9surmz3PSnUkvrQgjgMyjDF7AERkPnApsNV5gDFmucfxq4Fr7e3vA18YY47a534BTBeRFUCMMWaVnf4GMBNYdEp3468CAmDYTDjjEtj2Eax43AoEXz0Jk++GoTOtY9qR37y9HhHYfqiIZdtyKSqvAmBgt2gmD0rgg/UHuOPdjVw1rifvrzvAnBlDuHBEUp1ShVKqfr48FXoAWR7vs+20+tyI+0Fe37k97O1GrykiN4tImoik5eV5X2tW2QICYNgP4RcrYdarVqPxezfAC2fBlg+t3kRtzICu1voBN5zdB4AzkmIA2HG4iO2HigBcD3+AbQcLqax2cMe7GwF4+9ssKqodPPjJVm6d9x0ApRXukk9ZZdsuBSnVmnwJAN7q5r2Wu0XkWiAVeLKRc32+pjHmZWNMqjEmNSEhwYfsKgICrPECv1wFl/3L6kr67k/gxbNhy39bJxA4HJC/2wpESx+E/1wHS/7EpxcUsfnOUVw60or/N57Tl4Zag/YfPUHmEe8ziabtO0Z+cTnLtue60rKPnWjW21CqI/GlCigb6OnxPhnIqX2QiEwD7gUmG2PKPc6dUuvcFXZ6cq30OtdUpygg0OoiOuyH1oN3xWPw7vXQbbhVNTTkBy1TNVRZCrnb4NAmOLTZfqVDpf3gDgiCTj1hx0JCHVWEAiNj+7D1zNFEVB3gdcrZRi+qPP55Xj+xN6+v2se8Nfs5f0hXAIYkRvOj0T0oOFHJ8yt2A3D7fzYwdXBX13kHCsoY0DW6+e9RqQ7AlwCwFhgoIn2BA8Bs4GrPA0RkFPASMN0Yk+uxazHwZ4+G3wuAe4wxR0WkSEQmAGuAHwPPntqtqHp5BoL09+HLx+Gd66DbCJhyNwy+6OQDQUl+rQf9Zjiy0yp1AIREQ+IIGHWt9TdxBCQMgeAwa+qMgxshey1kf0tE1krY8QEfh0KZCWaT6cd6xwDWOwYyKakHr9sfudT+hf+nS4YxoZ81u+jQ7jHcOm89uYXlFHhML7FgQw6TB2nJUSlvfBoIJiIXAk8DgcCrxphHRORBIM0Ys0BElgAjgIP2KfuNMZfY5/4U+L2d/ogx5t92eirwGpP6qNcAACAASURBVBCO1Wbwa9NIZvx+IFhzcVRbvYS+fByO7rYeypPnwJCLqLf+xeGAgsyaD/pDm6HwgPuYmB7uh3xiivW3c2/fg4sxUHiA9DVLCDv8Hd2L0gk7spkARwUAFZHd+bywF7mdRvBJfg8e+sU1DOvl/rV/zwebWLj5EJec2Z03V+9zpe/+84UEBmgvY+W/dCSwqqu6CtKdgWCP9dCeMgf6nw9522s+6A+nQ3mhdZ4EQvwgSEpxP/C7jYDIuIY/72RUlVufn70Wsr4lZ8vXdMfqDGACQpCkFOg5DpJT+ex4T275OJeBXaMprawm+5i1+Pyy/5tMv4So5s+bUu2EBgBVv+oq2PwOfPkEHNuL1UZv/7sIibLaDFy/7EdA1zMgOLxVsnrNK6vZmZHB6IAMnp1URUjOOshZD1XWw/6w6cx6x0AORQ/HkTyWJzaFExEZzUe/OpuKagf9NRAoP6QBQDXOWSLIz4Buw6wSQWzfNjWG4E8LtvDaykwAMh+7yEqsroTDWyjevZLPP/+UUbKLvgGHAag0gaSbvqx1DCbNMYiX770VIuNbKfdKtY76AkDbn0pSnT6BQXDm7NbORYOcA71qVOkHBkP3kUQmncm720bzuz35TOsVwCvnQ9GOb6hIW8L1gZ9zc9Cn8ORTEDcQek2AXhOtv1361d/2oVQHpgFAtSuDullVOI/9KKXOPhHhyctTOOfx5YwYNAAGD6TL4Bks4mquW7mL4bKXsQE7+FVkHjHbP4H1b1onRnatGRASR1hBRakOTquAVLuzMauAlORO1Dd/YHlVNYEiBAVaVVcvfbmbRxdtr3HM9gcvIKxgN+xfBftXW38L7J5DwRGQnOoOCMljIVTHEqj2S6uAVIdxZs/ODe4PDao5ydy4vl3qHHPhs/9j2f9Nga5DIPUGK7Ewxw4GqyFrtTWXknFYq7EljnAHhJ4TICapuW5HqVajJQDlF3KLyng3LZuNWQV8vtVqIN7x8PQ6waKG8iKr+6mzhJCdBpX21BKxfTxKCOMgpjuEddK2BNUmaQlA+bWu0WH8auoAjDHc8NpaVuzIY1/+CQZ1a6BqJzQa+p9nvcDqbXRokzsgZCyBjW+7jw8IgvAuEBFnv3zYDonSoKFajQYA5VdEhDsuGMyKHXlk5BY3HABqCwyGHmOs18RfWSOXj+6BA+ugOBdO5Hu8jlpTYji3TT2zkgaGWIEgvEutIBFXK2B0gdAYq30iOMz6GxiiwaM+BVlwZIc1YLFTT/3vVA8NAMrv9EuIBGB3bvGpXUgE4vpbr4Y4HFB+3AoENYJEvkea/ffwFutv6THqmSDXMwM1A0JQmDVAz/kKCm9kn5f3oVHWQzMk8tT+25xODoc1cn3/Knej/nGPWegj4iBpJHQfZb9GWtOWaFDQAKD8T0RIED06h7M77xQDgK8CAiA81no1FiycHNVQWgCldmAoOWK1SVSVWrOtOl9VZVa7RKX91/m+4oR1XmVp3X2NkQCIGwBJZ1qDAZNSrL8RdRvTW0VVOeRsgP0r3Y32ZQXWvqhuVtvMxFutEev5u6yR4jkb4Jun3CWxyAR3QHAGBz9s2NcAoPxS77gI9h9tw2sFBARacytFxmGtmNpMjLEeoK6A4BlMSq2gc3iL1daxbyVsftd9bqdeVjDwDAzRSS3/S7rsOGStdT/wD6yz8g7WoL4zLobeZ1kN8rF9a+an32T3dmWpNS15znrrdXCD1Y5j7PUxohKt0oFnYIju1rL31so0ACi/1DU6lP9uyKGkvIrIUD/630DErhZqYOnMoZe4t0vy4dBGa9rug5uswLD9U1zVU5EJ7mDgDAynOn1I4UGP6pxVVkAyDmsSwqQzIfVG6D3R6o4b1YSpvoPDoedY6+VUUWJNNpizwR0Ydi523190d3e1kTMoNOUz2zg/+pevlJuz++ffvtjJH34wtJVz04ZFxtXsCQVWVdShdCsYHNxkBYeVz4LDXrozJNpdbeQMDPGDvI+uNgaO7HL/ut+3staAvLHW4kXOAXnN3TYREmmPAp9Q6/42uwNCzgbY8al7f0xyzZJCdKIVoIwBjJe/1JPexL+9Jjb7/es4AOWXDhSUcvZjyxjULYrPbptEgK4XcGqqyiF3q7uUcNBeJMiepZXAUOg21F1KqDzh7k57It86JiLe+mXvmpIjpe1MyVF23A52HiWFo3tObx5+tRYSBp3UqTobqFK1PLpwGy99tYc//3AEV4/v1drZ6Xgc1dbMss4HpzMwOBtsu/RzP+x7nWU1kLennjmlBVbpp/SYnW9p2t+mnpM4AkIiTiqrGgCUqqW8qpoR93/OhP5xvPHTca2dHf9gjNVFMzC0wzewtiX1BYC2M9G7UqdZaFAgM0d156udeTy6cBsFJypaO0sdnwh07qUP/zZCA4Dya33jremlX/pqD59vOez1mHs+2MTyHbmnM1tKnRY+BQARmS4iO0QkQ0TmeNk/SUS+E5EqEZnlkT5VRDZ4vMpEZKa97zUR2euxb2Tz3ZZSvokJd3eEu+v9TRSWVdbYX1nt4O1vs7jh32s5UFB6urOnVItqNACISCDwHDADGApcJSK1+83tB34CzPNMNMYsN8aMNMaMBM4DTgCfexxyp3O/MWbDyd+GUifne0O78YMU9wjQXYdrjg4uLHUHhL9+vuO05Uup08GXEsA4IMMYs8cYUwHMBy71PMAYk2mM2QQ4GrjOLGCRMaYND79U/qZrdBj/uHo0H996DgC/eGsdv3vH/Vvkb1/sdG3vzz+BMYYcLQmoDsKXANAD8JhZiWw7ralmA2/XSntERDaJyFMiEurtJBG5WUTSRCQtLy/vJD5WqcYlRFv//HKLyvnguwMAbD9UyNw1+wHoHBHM/qMnuPCZbzjrsWV8ln6w1fKqVHPxJQB465jbpL6jIpIEjAAWeyTfAwwBxgJdgLu9nWuMedkYk2qMSU1I6DhDsFXb0jW65u+PvKJypj/9tev9rNHJ5BaVs+1gIQCr9xxlydbDVFU3VOhVqm3zJQBkAz093icDOU38nCuAD40xrgpVY8xBYykH/o1V1aRUqwgIEJ6+ciQDu1q9gsY+ssS1Ly4yhJmjahZ6X1uZyU1vpLEw/RAAazOP8uzSXacvw0o1A18CwFpgoIj0FZEQrKqcBU38nKuoVf1jlwoQa2XvmUB6E6+pVLOaOaoHj12WUif9nVsm0jfePQdLzy7hru3fvL0egMtfXMVfv9ipJQLVrjQ6GZwxpkpEbsWqvgkEXjXGbBGRB4E0Y8wCERkLfAjEAheLyAPGmGEAItIHqwTxZa1LzxWRBKwqpg3ALc10T0qdtO6da86S+dnt59I/wSoVPHPVKMoqq4kJC+KWt75zHVNcXuXaPlxUzqrd+USHBfH9YYmnJ9NKnSSfZgM1xiwEFtZK+6PH9lqsqiFv52bipdHYGHNe3aOVal1xke62gMvHJDMkMcb1/pIzuwOw90hJjXOG3+9u2tqXX8Id724EIPOxi1oyq0qdMh0JrJSHkCD3/xJPXn6m12N6dbEm5Jo6uG6nhB2HilzbWh2k2joNAEp5kdo7tt59gQFC5mMX8e8bxvGE3WbQvZNVdeRZOhhw76KWzaRSp0gXhFGqlp0Pz8DX5QEmD06gb3wkt04dwP0LtpCZX3Oco8NhdK0B1WZpCUCpWkKCAggK9O1/jW4xYSy/YwqXjUmmW0wo+/Nrtg98vKmpPaaVOn00ACjVTLp3DneVAJ6+0prbcOk2nUVUtV0aAJRqJucMiHdtd42xehMt2JhDfnF5a2VJqQZpAFCqmfTs4l6uLzQokEHdrPEDzy7LaK0sKdUgDQBKNZNuMe5BZKFBAbz9swmANW2EUm2RBgClmonzFz9AdFgQcVFeJ7hVqs3QAKBUM4kOC3Z1H+0Za1UH/fq8AQAcOl7WWtlSql4aAJRqRl/eOZX3fzHR1fd/xnBrtbFPtDuoaoM0ACjVjHp2iWBM7y6u90O7x9A/IbLeBeeVak0aAJRqYecOTGBLznGMadI6Skq1OA0ASrWwHp3DKamoprCsqvGDlTqNNAAo1cIGJ0YDsHSbVgOptkUDgFIt7JwB8QzuFs0LK3af1Pm5RWWs33/spD8/p6CURZt1EXtVlwYApVpYQIBw2Zge7Mot5mhJRZPPv+yFlfzw+ZWUVVY3+dxHPt3KWY8t4xdzv6OorLLxE5Rf0QCg1GkwNKkTAJ/Zi8g3RdbRUgA2Hzje5HP/+fVe1/buvJIGjlT+SAOAUqfBxP5xdO8U1uR2gI82HHBtX/7iqiadW7vX0a7DRfUcqfyVBgClToPAAGH68CSWbs+lz5xP2ZhV4NN5mUdqLjDTlId4SYVVZTTtjK6IwKo9+b5nWPkFnwKAiEwXkR0ikiEic7zsnyQi34lIlYjMqrWvWkQ22K8FHul9RWSNiOwSkf+ISMip345SbdfscT1d25c+9z9+/Oq3FJxouE3gRGUVIUEBfH3XVAC+3Jnn2vdOWhbvrM2q99xnl+4CrEboS8/szgffHcDhcJcKqh1Gxyb4uUYDgIgEAs8BM4ChwFUiMrTWYfuBnwDzvFyi1Bgz0n5d4pH+OPCUMWYgcAy48STyr1S7MbBrVI33X+3M477/ptd7fFllNS99uYcAsUYYR4cG8fCn21wP8bve28Rd72/yem5+cTkvfbUHgLioUIZ1t9og/utRpTTovkX8/sPNp3RPqn3zpQQwDsgwxuwxxlQA84FLPQ8wxmQaYzYBDl8+VEQEOA94z056HZjpc66VaodEhB0PT+cHKUmutIzc4nqPX7P3KABV1dYDv7zK+t9rUfqhGiWH2r17Pks/yKOLtgPw4rWjuWhEEleP7wXAh+utAFBZ7aDaYXj72yxKynWAmr/yJQD0ADzLmdl2mq/CRCRNRFaLiPMhHwcUGGOc//LqvaaI3Gyfn5aXl+ftEKXajdCgQG6Z3B+wRghvP1TEjL9/TUVV3d9OH36XDcC/fjIWgHdvmQjAr+Z9x7lPLHcdt+2gu12guLyKW976jvfWZZMYE8b04UkEBAiRoUEAfL3rCCszjlDkMSr5lrfWNfNdqvbClwAgXtKaUnHYyxiTClwNPC0i/ZtyTWPMy8aYVGNMakJCQhM+Vqm2aXiPTmx/aDrPXDUKgG0HC3nis+016uerqh38d4M1g+j4vtbkcmf27Oza7/kA35Lj7h56xzsbXduxkTWb1ebMGALAt5lHKSx1lxq+3nXklO9JtU++BIBsoKfH+2TA57ltjTE59t89wApgFHAE6CwiQSdzTaXau7DgQMb0jiXa/mX+yjd7+dieMtoYw4B7F9U41ik5NrzOtbbkFLq2P9viHmew7WBhjeNumdyfYd1jWLLtsCuAOK+n01T4J18CwFpgoN1rJwSYDSxo5BwARCRWRELt7XjgbGCrsboeLAecPYauBz5qauaVau823n+Ba/vJxTvIKSjl7W/dNa4PXjqsxvHPXzOaV36c6no/vEcMW3IKMcawNtNqM/hBShJ94yNdVU2ehnfvRPqBQuav3Q/AE5elAFoK8FdBjR1gjKkSkVuBxUAg8KoxZouIPAikGWMWiMhY4EMgFrhYRB4wxgwDzgBeEhEHVrB5zBiz1b703cB8EXkYWA/8q9nvTqk2LiBAGN4jhvQDhWQfK+Wsx5bV2D99eGKN9ynJVjXQ8jumEB0WxNNLdvLW6v30vWeh65irx/XirAHxXj/v55P78Z+0LOau2U90WBDj+8UxsmdndhzSQWL+qNEAAGCMWQgsrJX2R4/ttVjVOLXPWwmMqOeae7B6GCnl1975+UTmrdnPw59uq5H+2e3n0jU6zOs5feMjAbg4pTtvrd5fY9+wHp3q/ax+Ce6uqAO7RhEYIAzuFs0SrQLySzoSWKlWFhESxJTBXQEIDnT3j+gS2fjYyPH94pg5snuNNGe7QmOuGmd1DR2UGE1+SQVHisvJLy7X9Yv9iG//UpRSLWpA1yjSH/g+JeVVXP7iKlKSO5EQFerTuYmdajYMO9cjrs9Dlw5j3b5jXJ5q9e0Y3M1ar2DnoSKufmUN3WJCWfP7aSdxF6q90QCgVBsRFRpEVGgQX9nTPvjq1+cNICo0kDG9u5B97ESjx183sQ/XTezjet83wapOemrJTgAOF5ZTXF5FlI8lCdV+6TesVDsXGRrErecNtN/FNfn8xBirnWFtpnvRmYWbD3JFas/6TlEdhLYBKOXnAr1UGb381Z4aA9NUx6QBQCnlCgK/nNKfM3t2JiO3mEH3LeK7U1iKUrV9GgCUUkweZE2zMmtMMj89uw8AVQ7Dj55fyfETupRkR6UBQCnFP64exXNXj6ZfQhQzhidx8ZndcdYM/fadDa2bOdViNAAopYgICeIie5rqkKAAnr1qFOvu+x4Ay7bnUlrR9AXp24uqagdvrMqkvKrj3mN9NAAopbzynE308611F7OvdhiOl7b/6qGPNuTwx4+2cMe7m6j2s4ZvDQBKqXrNu2k8AN/ai9N4euiTrZz5wOfsyy853dlqVhXV1loMH2/M4aFPtjZydMeiAUApVa+zBsTTJy6CuWv2k3mk5oP+s3SrVLBse26d8yqqHDy6aBuHC9v+tBJPfLbdte255rI/0ACglGpQlV0tUnv9YOcv5335dUcfr9qTz0tf7uH3H/i25nBGbjFz1+xrUr7KKqtZv//YKS1sX1Xt4Jjdy+mK1GTyi8tP6XrtjQYApVSD5t00AYCVu/NdjcHGGNeqYq+tzGTJVvdsoocLy/izPbPp0u25rMxofK2B37y9nns/TK9TymjIP5Zl8MPnV7LCx1/txhh2HS5yPeDzispdC++M7tWZoUkxFJZVkVtUDsDeIyUdfr1kDQBKqQb1iovgrumDATjjj5+RlnmUskqHq2QAcNMbaey1H97XvLKGHYfd6wt8vvUwCzbm8Pa3++udq+hEhfWg/TazbluDN1XVDv6xPAOARZsPNnp8WWU1zy7L4HtPfcUjdnA6eLzUtf8fV49mkD0p3q7DxQBM/csKZr+8usHrVlY7KDhR4VOe2yINAEqpRt1wVl/X9qwXV/HSV7vrHPP4Iqsu/WiJ9UB89qpRDEmM5t20LH7z9nru+WAz5zy+3OsDMyjQehRtzSmss8+bPR4lhU83HaSssuEunLfP38DfvrAmu/tkkxUwSsqtcx64ZBjdO4cz0A4AOw4XUWlXb20+cJyjJRVU2e89HTxeysRHlzHywS/a7bQZGgCUUo0KDwnk29+fzzn2SmNPL9kFwL0XnsF1E3pz0YgkvtyZR1W1g/LKaq6f2JuLz+xOn7hISmqNIfjguwOu7ZLyKu7/KJ2MXOtX92srMyn2odplT547AJRUVDPkD5/ZAWZZnWM3ZBXUWCv5UGEZf1qwxVXqGNXLWmUtPiqE7p3CSMs8yolyd55HP/QFf164vcY1S8qrmPjoMo4UW9VFe44UN5rntkgDgFLKJ11jwnjrpvG8fN0YV9qMEYk8NHM4kwcnUFpZzao9+ZRUVDPA/jUdEepe0P7Gc/qS1CmMBz/ZyrS/fUl5VTXD7l/M66tqNv7+nw8jj52ljM9uP9eVZlUxlbp+vTvtP1q32um1lZmuwBQRYk2KLCKcNSCeVXvyOVqrlPL6qswa759cvKPG+483Nl4N1RZpAFBKNcmkQQl0Cg/mzz8cQXJshJU2MIEAgddXZgKQEGUNIrvt/IEM7xHDDWf34b6LziA51lq8JiO3mFEPflHjuleNs6afzilouOvo17vy2JNn/eLuExfJqnvOq7HfWYfvVFRmNVb/+Ycj+M/NE1zpm7IKAIj0CFKpvWMpOFHJ1L+sAODWqQMASOpUc2nOxVtqDoz7+9JdXquJ2jqfAoCITBeRHSKSISJzvOyfJCLfiUiViMzySB8pIqtEZIuIbBKRKz32vSYie0Vkg/0a2Ty3pJRqSWHBgWy8/wKuHt/LlZbYKYwRyZ1Zss0aE9Al0lrNrHdcJJ/8+lzuv3gYIsJfLj+TK+11Bk7Uqhq6/qw+XDgikZKK+quAKqsdXPevb3nlm72EBQcQFhxIUq0V0T7acIBPNuUw5/1NpB84zr0fpgPwo9E9GN8vjoW/sUoNr3yzF7DWU3BytgM4jUjuxI3n9OVIcbmrnj/r6AkOHi/j/ouH8rXH4j17m9CDqa1oNACISCDwHDADGApcJSJDax22H/gJMK9W+gngx8aYYcB04GkR6eyx/05jzEj7pTNOKdWOnTPAvRhNbESw12N6x0Xy+KwUenZxP7R/PqkfveMiGNQ1mqRO4ezJK+Hlr3Z7nYX0WIm7aqay2t3w6iw99Ogczuq9R7l13nrmr83ishdWuo4JC7Z+6Q/tHsMZSTEAxEWGEBPmzuvAblE1Pm9M71gGdI2irNLBgQKr19DyHVaQO3dgPD27RPDwzOEAfLD+AO2NLyWAcUCGMWaPMaYCmA9c6nmAMSbTGLMJcNRK32mM2WVv5wC5QEKz5Fwp1aac3d9qIE6ODa/zS7q2FXdM5e7pQ/j77JHcc+EZfHnnVAIChO8PSwTgzwu3M+2pL+ucN+7PS13bnsvYPPqjFDIfu4jzhnRlo121A1BeZT2SXv/puBrXudoOGLUXw/EMBv+4ehTxUaGu7qHPr8igosrBw59uY1C3KAZ0tdKvndCboUkxvLBiN1f/czXPr8gg/cBxnxqzG1PtMDWCXnPzJQD0ALI83mfbaU0iIuOAEMCz/9gjdtXQUyLidQVsEblZRNJEJC0vz7+GaSvVnkzsH8fcm8az4o4pjR4bGCD8Ykp/Lh1Z81Eyrm8X+sVbaxTn2QOynDxH6F40Iom37HmKPH1vaLc629eM7+Va78DJGWgGJ9YNVH+fPZIhidFM7GeVaEb17Ez3TmF8vPEg9y/YQkWVgwuGJtY4Z5J9/ZW783nisx384Nlv+OvnO8g6eqLOdBilFdXM+PvXvLEqs85n1/a3L3Yw6qEvuPzFlY0eezJ8CQB114uDJnV6FZEk4E3gBmOMs5RwDzAEGAt0Ae72dq4x5mVjTKoxJjUhQQsPSrVVIsLZA+JdffpP1vVn9XFtewaBw4Xu7cdnpTChX931j88eEE9ybDgPXTrM1b0zJKhufrrGhPH2zybw9JV1mx4vHdmDz26fRFyU9Zs0IED4+eT+FJdX8emmHAB+ObV/jXNmjan7m/irnXlMfnI54/+81FUqqahycMd7G9l2sJA/frSFQ8fL6vRa8uScm8hzvebm5Ms3lQ14rg6dDOT4+gEiEgN8CtxnjHENqzPGHDSWcuDfWFVNSik/N2tMMr26WL2L7vlgkyvd2df+sR+NIMqj4dZTYIDwzd3ncd3EPnSLtnruRIQEej12Yv8410O+MRcMs0oThWVVzBie6Oo66jSgazTv3jKRz387yZW2O68E5/iwX7+9nrLKav6xPINPN7m7jE54dCk/fP5/9U5DPciuZpo0qGV+/PoSANYCA0Wkr4iEALOBBb5c3D7+Q+ANY8y7tfYl2X8FmAmkNyXjSqmOKTI0iC/vnALAzsPFFJyoYE9eMQ99Yk3hMGVwV5+uc8nI7tw1fTC/nDLglPPk2dNo3T7vv8bH9unCoG7RjO7Vuc6+/UdPcOYDn/PM0l119qUfKORTj+ksPBffiQqzAs1fLz/zpPPeEO9h1IMxpkpEbgUWA4HAq8aYLSLyIJBmjFkgImOxHvSxwMUi8oDd8+cKYBIQJyI/sS/5E7vHz1wRScCqYtoA3NLcN6eUap9EhBvP6cu/vtnLhEeXUlbpriZJrNUnvz7BgQHN8vB3WnDr2Vzyj//x+wvPaPC4t24az/8y8vnZG2kA/HbaIJ5astPVIO312hty2JhVQERIIM8uy+DCEYkkRIWSV1ROYkwYCdG+lVSaStrT1KepqakmLS2ttbOhlDoNvtqZx49f/bZG2tybxnO2PR1FW1ZWaU1PAbByznmc9VjNKSoW3XYu1/3rWyYPSiAqNLDOaOjaMh+76JTyIyLrjDGptdN1JLBSqk2aNCiBsX1ia6SN7dOllXLTNM4xBwDdO4eTHBvOrDHJAMwYnsgZSTGsvfd8/nJ5ChP7123MPl0arQJSSqnWcuM5/VibuQ6wBnl569HTVq27b5prsZlv7ramq5gzY4irAdtq/oTpw5Nc5/zue4MY1C2K8ioHt823xsbePm1gi+VRA4BSqs0622N08WKPHjbtQVxUaJ1eRvH19Dq698IzSIgOZeYoqztpXlE5InDZ6GRunzaoxfKobQBKqTZt7pp99IuPatWqktaQX1xO54iQOqOVT0Z9bQBaAlBKtWnXjO/d2lloFb6OUTgV7adCTSmlVLPSAKCUUn5KA4BSSvkpDQBKKeWnNAAopZSf0gCglFJ+SgOAUkr5KQ0ASinlp9rVSGARyQManjbPu3jgSDNnp63Qe2u/OvL9deR7g/Z3f72NMXVWlWlXAeBkiUiat2HQHYHeW/vVke+vI98bdJz70yogpZTyUxoAlFLKT/lLAHi5tTPQgvTe2q+OfH8d+d6gg9yfX7QBKKWUqstfSgBKKaVq0QCglFJ+qkMHABGZLiI7RCRDROa0dn6aSkR6ishyEdkmIltE5DY7vYuIfCEiu+y/sXa6iMgz9v1uEpHRrXsHjRORQBFZLyKf2O/7isga+97+IyIhdnqo/T7D3t+nNfPtCxHpLCLvich2+zuc2FG+OxH5rf1vMl1E3haRsPb83YnIqyKSKyLpHmlN/q5E5Hr7+F0icn1r3EtTdNgAICKBwHPADGAocJWIDG3dXDVZFfB/xpgzgAnAr+x7mAMsNcYMBJba78G614H262bghdOf5Sa7Ddjm8f5x4Cn73o4BN9rpNwLHjDEDgKfs49q6vwOfGWOGAGdi3We7/+5EpAfwGyDVGDMcCARm076/u9eA6bXSmvRdiUgX4H5gPDAOuN8ZNNosY0yHfAETgcUe7+8B7mntfJ3iPX0EfA/YASTZaUnADnv7JeAqj+Ndx7XFF5CM9T/WJuOgqAAAArFJREFUecAngGCNrgyq/R0Ci4GJ9naQfZy09j00cG8xwN7aeewI3x3QA8gCutjfxSfA99v7dwf0AdJP9rsCrgJe8kivcVxbfHXYEgDuf6RO2XZau2QXm0cBa4BuxpiDAPbfrvZh7e2enwbuAhz2+zigwBhTZb/3zL/r3uz9x+3j26p+QB7wb7uK6xURiaQDfHfGmAPAX4D9wEGs72IdHee7c2rqd9VuvkOnjhwAxEtau+zzKiJRwPvA7caYwoYO9ZLWJu9ZRH4A5Bpj1nkmeznU+LCvLQoCRgMvGGNGASW4qxC8aTf3Z1drXAr0BboDkVjVIrW11++uMfXdT7u7z44cALKBnh7vk4GcVsrLSRORYKyH/1xjzAd28mERSbL3JwG5dnp7uuezgUtEJBOYj1UN9DTQWUSC7GM88++6N3t/J+Do6cxwE2UD2caYNfb797ACQkf47qYBe40xecaYSuAD4Cw6znfn1NTvqj19h0DHDgBrgYF2z4QQrEaqBa2cpyYREQH+BWwzxvzNY9cCwNnD4HqstgFn+o/tXgoTgOPOImxbY4y5xxiTbIzpg/XdLDPGXAMsB2bZh9W+N+c9z7KPb7O/rowxh4AsERlsJ50PbKUDfHdYVT8TRCTC/jfqvLcO8d15aOp3tRi4QERi7VLSBXZa29XajRAt+QIuBHYCu4F7Wzs/J5H/c7CKkJuADfbrQqz606XALvtvF/t4wer5tBvYjNVLo9Xvw4f7nAJ8Ym/3A74FMoB3gVA7Pcx+n2Hv79fa+fbhvkYCafb3918gtqN8d8ADwHYgHXgTCG3P3x3wNlZ7RiXWL/kbT+a7An5q32cGcENr31djL50KQiml/FRHrgJSSinVAA0ASinlpzQAKKWUn9IAoJRSfkoDgFJK+SkNAEop5ac0ACillJ/6f5sVULxwbJAwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the training loss keeps getting better and better. But notice that eventually the validation loss improvement slows, and sometimes even gets worse! This is the point at which the model is starting to over fit. In particular, the model is becoming overconfident of its predictions. But this does *not* mean that it is getting less accurate, necessarily. Have a look at the table of training results per epoch, and you will often see that the accuracy continues improving, even as the validation loss gets worse. In the end what matters is your accuracy, or more generally your chosen metrics, not the loss. The loss is just the function we've given the computer to help us to optimise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting the number of epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often you will find that you are limited by time, rather than generalisation and accuracy, when choosing how many epochs to train for. So your first approach to training should be to simply pick a number of epochs that will train in the amount of time that you are happy to wait for. Have a look at the training and validation loss plots, likely showed above, and in particular your metrics, and if you see that they are still getting better even in your final epochs, then you know that you have not trained for too long.\n",
    "\n",
    "On the other hand, you may well see that the metrics you have chosen are really getting worse at the end of training. Remember, it's not just that were looking for the validation loss to get worse, but your actual metrics. Your validation loss will first of all during training get worse because it gets overconfident, and only later will get worse because it is incorrectly memorising the data. We only care in practice about the latter issue. Our loss function is just something, remember, that we used to allow our optimiser to have something it could differentiate and optimise; it's not actually the thing we care about in practice.\n",
    "\n",
    "Before the days of 1cycle training it was very common to save the model at the end of each epoch, and then select whichever model had the best accuracy, out of all of the models saved in each epoch. This is known as *early stopping*. However, with one cycle training, it is very unlikely to give you the best answer, because those epochs in the middle occur before the learning rate has had a chance to reach the small values, where it can really find the best result. Therefore, if you find that you have overfit, what you should actually do is to retrain your model from scratch, and this time select a total number of epochs based on where your previous best results were found."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deeper architectures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we've got the time to train for more epochs, we may want to instead use that time to train more parameters. In general, a model with more parameters can model your data more accurately. (There are lots and lots of caveats to this generalisation, and it depends on the specifics of the architectures you are using, but it is a reasonable rule of thumb for now.) For most of the architectures that we will be seeing in this book you can create larger versions of them by simply adding more layers. However, since we want to use pretrained models, we need to make sure that we choose a number of layers that has been already pretrained for us.\n",
    "\n",
    "This is why, in practice, architectures tend to come in a small number of variants. For instance, the resnet architecture that we are using in this chapter comes in 18, 34, 50, 101, and 152 layer variants, pre-trained on ImageNet. A larger (more layers and parameters; sometimes described as the \"capacity\" of a model) version of a resnet will always be able to give us a better training loss, but it can suffer more from overfitting, because it has more parameters to over fit with.\n",
    "\n",
    "In general, a bigger model has the ability to better capture the real underlying relationships in your data, and also to capture and memorise the specific details of your individual images.\n",
    "\n",
    "However, using a deeper model is going to require more GPU RAM, so we may need to lower the size of our batches to avoid *out-of-memory errors*. This happens when you try to fit too much inside your GPU and looks like:\n",
    "\n",
    "```\n",
    "Cuda runtime error: out of memory\n",
    "```\n",
    "\n",
    "You may have to restart your notebook when this happens, and the way to solve it is to use a smaller *batch size*, which means we will pass smaller groups of images at any given time through our model. We can pass the batch size we want to the call creating our `DataLoaders` with `bs=`.\n",
    "\n",
    "The other downside of deeper architectures is that they take quite a bit longer to train. One thing that can speed things up a lot is *mixed precision training*. This refers to using less precise numbers (*half precision floating point*, also called *fp16*) where possible during training. As we are writing this words (early 2020) nearly all current NVIDIA GPUs support a special feature called *tensor cores* which can dramatically (2x-3x) speed up neural network training. They also require a lot less GPU memory. To enable this feature in fastai, just add `to_fp16()` after your `Learner` creation (you also need to import the module).\n",
    "\n",
    "You can't really know ahead of time what the best architecture for your particular problem is, until you try training some. So let's try a resnet 50 now with mixed precision:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.427505</td>\n",
       "      <td>0.310554</td>\n",
       "      <td>0.098782</td>\n",
       "      <td>00:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.606785</td>\n",
       "      <td>0.302325</td>\n",
       "      <td>0.094723</td>\n",
       "      <td>00:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.409267</td>\n",
       "      <td>0.294803</td>\n",
       "      <td>0.091340</td>\n",
       "      <td>00:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>error_rate</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.261121</td>\n",
       "      <td>0.274507</td>\n",
       "      <td>0.083897</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.296653</td>\n",
       "      <td>0.318649</td>\n",
       "      <td>0.084574</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.242356</td>\n",
       "      <td>0.253677</td>\n",
       "      <td>0.069012</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.150684</td>\n",
       "      <td>0.251438</td>\n",
       "      <td>0.065629</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.094997</td>\n",
       "      <td>0.239772</td>\n",
       "      <td>0.064276</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.061144</td>\n",
       "      <td>0.228082</td>\n",
       "      <td>0.054804</td>\n",
       "      <td>00:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fastai2.callback.fp16 import *\n",
    "learn = cnn_learner(dls, resnet50, metrics=error_rate).to_fp16()\n",
    "learn.fine_tune(6, freeze_epochs=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll see here we've gone back to using `fine_tune`, since it's so handy! We can pass `freeze_epochs` to tell fastai how many epochs to train for while frozen. It will automatically change learning rates appropriately for most datasets.\n",
    "\n",
    "In this case, we're not seeing a clear win from the deeper model. This is useful to remember--bigger models aren't necessarily better models for your particular case! Make sure you try small models before you start scaling up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this chapter we learned some important practical tips, both for getting our image data ready for modeling (presizing; data block summary) and for fitting the model (learning rate finder, unfreezing, discriminative learning rates, setting the number of epochs, and using deeper architectures). Using these tools will help you to build more accurate image models, more quickly.\n",
    "\n",
    "We also learned about cross entropy loss. This part of the book is worth spending plenty of time on. You aren't likely to need to actually implement cross entropy loss from scratch yourself in practice, but it's really important you understand the inputs to and output from that function, because it (or a variant of it, as we'll see in the next chapter) is used in nearly everything classification model. So when you want to debug a model, or put a model in production, or improve the accuracy of a model, you're going to need to be able to look at its activations and loss, and understand what's going on, and why. You can't do that properly if you don't understand your loss function.\n",
    "\n",
    "If cross entropy loss hasn't \"clicked\" for you just yet, don't worry--you'll get there! First, go back to the last chapter and make sure you really understand `mnist_loss`. Then work gradually through the cells of the notebook for this chapter, where we step through each piece of cross entropy loss. Make sure you understand what each calculation is doing, and why. Try creating some small tensors yourself and pass them into the functions, to see what they return.\n",
    "\n",
    "Remember: the choices made in cross entropy loss are not the only possible choices that could have been made. Just like when we looked at regression, we could choose between mean squared error and mean absolute difference (L1), we could change the details inside cross entropy loss too. If you have other ideas for possible functions that you think might work, feel free to give them a try in this chapter's notebook! (Fair warning though: you'll probably find that the model will be slower to train, and less accurate. That's because the gradient of cross entropy loss is proportional to the difference between the activation and the target, so SGD always gets a nicely scaled step for the weights.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questionnaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Why do we first resize to a large size on the CPU, and then to a smaller size on the GPU?\n",
    "1. If you are not familiar with regular expressions, find a regular expression tutorial, and some problem sets, and complete them. Have a look on the book website for suggestions.\n",
    "1. What are the two ways in which data is most commonly provided, for most deep learning datasets?\n",
    "1. Look up the documentation for `L` and try using a few of the new methods is that it adds.\n",
    "1. Look up the documentation for the Python pathlib module and try using a few methods of the Path class.\n",
    "1. Give two examples of ways that image transformations can degrade the quality of the data.\n",
    "1. What method does fastai provide to view the data in a DataLoader?\n",
    "1. What method does fastai provide to help you debug a DataBlock?\n",
    "1. Should you hold off on training a model until you have thoroughly cleaned your data?\n",
    "1. What are the two pieces that are combined into cross entropy loss in PyTorch?\n",
    "1. What are the two properties of activations that softmax ensures? Why is this important?\n",
    "1. When might you want your activations to not have these two properties?\n",
    "1. Calculate the \"exp\" and \"softmax\" columns of <<bear_softmax>> yourself (i.e. in a spreadsheet, with a calculator, or in a notebook).\n",
    "1. Why can't we use torch.where to create a loss function for datasets where our label can have more than two categories?\n",
    "1. What is the value of log(-2)? Why?\n",
    "1. What are two good rules of thumb for picking a learning rate from the learning rate finder?\n",
    "1. What two steps does the fine_tune method do?\n",
    "1. In Jupyter notebook, how do you get the source code for a method or function?\n",
    "1. What are discriminative learning rates?\n",
    "1. How is a Python slice object interpreted when past as a learning rate to fastai?\n",
    "1. Why is early stopping a poor choice when using one cycle training?\n",
    "1. What is the difference between resnet 50 and resnet101?\n",
    "1. What does to_fp16 do?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further research"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Find the paper by Leslie Smith that introduced the learning rate finder, and read it.\n",
    "1. See if you can improve the accuracy of the classifier in this chapter. What's the best accuracy you can achieve? Have a look on the forums and book website to see what other students have achieved with this dataset, and how they did it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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